Responsibilities
- Lead strategic planning, hiring, and management in foundational research.
- Mentor, lead, and help direct reports grow and contribute to the wider group.
- Innovate and create new state-of-the-art Agent AI/LLM Agent approaches at the cutting edge of AI research.
- Contribute ideas and work on solving real-world challenges using a wealth of data in agentic contexts.
- Participate in the entire research & model development lifecycle: brainstorming, coding, testing, and delivering high-quality reports at leading international academic conferences.
- Collaborate with a global team of research engineers within Thomson Reuters and academic partners at world-leading universities.
- Share technical findings with the wider community through seminars, lectures, conferences, publications, and/or technical assets (data & models).
Requirements
- PhD in a relevant discipline.
- 3+ years of hands-on experience leading teams building advanced ML / NLP / AI systems in academia (e.g. through student supervision) or industry.
- Strong publication record in top-tier conferences (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL, ICLR) with specific focus on agent systems, tool use, or multi-agent coordination.
- Familiarity with one or more deep learning frameworks (e.g. pytorch, jax, tensorflow, …).
- Experience in ML Research beyond completing a PhD (e.g. supervision, industry experience, leading academic initiatives, …).
- Excellent communication skills to report and present research findings and developments clearly, both orally and in writing.
- Curious and innovative disposition capable of devising novel, well-founded algorithmic solutions to relevant problems.
- Good social skills and ability to motivate, inspire and mentor team members.
- Comfortable in working in fast-paced, agile environments, managing uncertainty and ambiguity.
Nice to Have
- High-impact publications in top-tier conferences or other influence in the research community.
- 5+ years of hands-on experience leading teams building advanced ML / NLP / IR systems in academia (e.g. through student supervision) or for commercial applications.
- Extensive experience with deep learning and large-scale model training.
- Extensive experience working on agent-based systems, tool-using AI, or multi-agent coordination in LLM contexts (e.g. startup, industry, or extensive open-source experience).
- Strong software and/or infrastructure engineering skills and ensuring well-managed software delivery, as evidenced by code contributions to popular open-source libraries or writing production code.
- Experience training large-scale models over distributed nodes with cloud tools such as Amazon AWS, MS Azure, or Google Cloud.
